Evaluating Patient Readmission Risk: A Predictive Analytics Approach
Computers and Society
2019-08-22 v2 Machine Learning
Machine Learning
Abstract
With the emergence of the Hospital Readmission Reduction Program of the Center for Medicare and Medicaid Services on October 1, 2012, forecasting unplanned patient readmission risk became crucial to the healthcare domain. There are tangible works in the literature emphasizing on developing readmission risk prediction models; However, the models are not accurate enough to be deployed in an actual clinical setting. Our study considers patient readmission risk as the objective for optimization and develops a useful risk prediction model to address unplanned readmissions. Furthermore, Genetic Algorithm and Greedy Ensemble is used to optimize the developed model constraints.
Keywords
Cite
@article{arxiv.1812.11028,
title = {Evaluating Patient Readmission Risk: A Predictive Analytics Approach},
author = {Avishek Choudhury and Christopher M Greene},
journal= {arXiv preprint arXiv:1812.11028},
year = {2019}
}
Comments
arXiv admin note: text overlap with arXiv:1403.1210 by other authors